New insights into radiative transfer within sea ice derived from autonomous optical propagation measurements

Katlein, Christian; Valcic, Lovro; Lambert-Girard, Simon; Hoppmann, Mario

doi:10.5194/tc-15-183-2021

Cited by 18 publications

(27 citation statements)

References 55 publications

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“…During AO18, substantial noise in the snow depth retrieved using red wavelengths is attributed to irradiances below 1.0 mWm −2 and transmittance below 1% (Figure 8, Katlein et al, 2021). From 14 September onwards noise is also evident in the snow depth retrieved using blue wavelengths (Figure 8B).…”

Section: Atmospheric Influencementioning

confidence: 97%

Snow Depth Retrieval on Arctic Sea Ice Using Under-Ice Hyperspectral Radiation Measurements

et al. 2021

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Radiation transmitted through sea ice and snow has an important impact on the energy partitioning at the atmosphere-ice-ocean interface. Snow depth and ice thickness are crucial in determining its temporal and spatial variations. Under-ice surveys using autonomous robotic vehicles to measure transmitted radiation often lack coincident snow depth and ice thickness measurements so that direct relationships cannot be investigated. Snow and ice imprint distinct features on the spectral shape of transmitted radiation. Here, we use those features to retrieve snow depth. Transmitted radiance was measured underneath landfast level first-year ice using a remotely operated vehicle in the Lincoln Sea in spring 2018. Colocated measurements of snow depth and ice thickness were acquired. Constant ice thickness, clear water conditions, and low in-ice biomass allowed us to separate the spectral features of snow. We successfully retrieved snow depth using two inverse methods based on under-ice optical spectra with 1) normalized difference indices and 2) an idealized two-layer radiative transfer model including spectral snow and sea ice extinction coefficients. The retrieved extinction coefficients were in agreement with previous studies. We then applied the methods to continuous time series of transmittance and snow depth from the landfast first-year ice and from drifting, melt-pond covered multiyear ice in the Central Arctic in autumn 2018. Both methods allow snow depth retrieval accuracies of approximately 5 cm. Our results show that atmospheric variations and absolute light levels have an influence on the snow depth retrieval.

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Section: Atmospheric Influencementioning

confidence: 97%

Snow Depth Retrieval on Arctic Sea Ice Using Under-Ice Hyperspectral Radiation Measurements

et al. 2021

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“…We then enter what we call the sub-diffusive regime (N = 2). In that case, a second moment need to be included in p(θ ) for precise calculation of apparent optical properties, including R. Bevilacqua and Depeursinge (1999) and Kienle et al (2001) stress that, in a reflectance geometry, for 0.5 < ρb < 5, the second-order moment g 2 needs to be set independently of g 1 in Eq. (1) to correctly calculate R. Sea ice values found in literature for medium-to high-scattering ice (b = 10 1 to 10 2 m −1 ) mean that the N = 2 regime is met for ρ on the order of a few centimetres.…”

Section: Sub-diffusive Regimementioning

confidence: 99%

Development of a diffuse reflectance probe for in situ measurement of inherent optical properties in sea ice

et al. 2021

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Abstract. Detailed characterization of the spatially and temporally varying inherent optical properties (IOPs) of sea ice is necessary to better predict energy and mass balances, as well as ice-associated primary production. Here we present the development of an active optical probe to measure IOPs of a small volume of sea ice (dm3) in situ and non-destructively. The probe is derived from the diffuse reflectance method used to measure the IOPs of human tissues. The instrument emits light into the ice by the use of an optical fibre. Backscattered light is measured at multiple distances away from the source using several receiving fibres. Comparison to a Monte Carlo simulated lookup table allows, in theory, retrieval of the absorption coefficient, the reduced scattering coefficient and a phase function similarity parameter γ, introduced by Bevilacqua and Depeursinge (1999). γ depends on the two first moments of the Legendre polynomials, allowing the analysis of the backscattered light not satisfying the diffusion regime. The depth reached into the medium by detected photons was estimated using Monte Carlo simulations: the maximum depth reached by 95 % of the detected photons was between 40±2 and 270±20 mm depending on the source–detector distance and on the ice scattering properties. The magnitude of the instrument validation error on the reduced scattering coefficient ranged from 0.07 % for the most scattering medium to 35 % for the less scattering medium over the 2 orders of magnitude we validated. Fixing the absorption coefficient and γ, which proved difficult to measure, vertical profiles of the reduced scattering coefficient were obtained with decimetre resolution on first-year Arctic interior sea ice on Baffin Island in early spring 2019. We measured values of up to 7.1 m−1 for the uppermost layer of interior ice and down to 0.15±0.05 m−1 for the bottommost layer. These values are in the range of polar interior sea ice measurements published by other authors. The inversion of the reduced scattering coefficient at this scale was strongly dependent on the value of γ, highlighting the need to define the higher moments of the phase function. This newly developed probe provides a fast and reliable means for measurement of scattering in sea ice.

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“…An important difference between our system and commercially available buoys is the presence of several light sensors in the air, in the ice and in the water which allows for estimating the attenuation of solar radiation in the ice and snow covers and in the water column. In this regard, the experimental development of a chain of 48 side-ward viewing multispectral irradiance sensors at 5 cm vertical spacing [45] is of particular interest. Its first deployment showed that the sensor chain successfully captures the spatiotemporal variation of the in-ice light field from the surface through the ice to the underlying water.…”

Section: Discussionmentioning

confidence: 99%

Autonomous System for Lake Ice Monitoring

Aslamov

Kirillin

Макаров

et al. 2021

Sensors

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Continuous monitoring of ice cover belongs to the key tasks of modern climate research, providing up-to-date information on climate change in cold regions. While a strong advance in ice monitoring worldwide has been provided by the recent development of remote sensing methods, quantification of seasonal ice cover is impossible without on-site autonomous measurements of the mass and heat budget. In the present study, we propose an autonomous monitoring system for continuous in situ measuring of vertical temperature distribution in the near-ice air, the ice strata and the under-ice water layer for several months with simultaneous records of solar radiation incoming at the lake surface and passing through the snow and ice covers as well as snow and ice thicknesses. The use of modern miniature analog and digital sensors made it possible to make a compact, energy efficient measurement system with high precision and spatial resolution and characterized by easy deployment and transportation. In particular, the high resolution of the ice thickness probe of 0.05 mm allows to resolve the fine-scale processes occurring in low-flow environments, such as freshwater lakes. Several systems were tested in numerous studies in Lake Baikal and demonstrated a high reliability in deriving the ice heat balance components during ice-covered periods.

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New insights into radiative transfer within sea ice derived from autonomous optical propagation measurements

Cited by 18 publications

References 55 publications

Snow Depth Retrieval on Arctic Sea Ice Using Under-Ice Hyperspectral Radiation Measurements

Snow Depth Retrieval on Arctic Sea Ice Using Under-Ice Hyperspectral Radiation Measurements

Development of a diffuse reflectance probe for in situ measurement of inherent optical properties in sea ice

Autonomous System for Lake Ice Monitoring

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